The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Haze is an atmospheric phenomenon of water vapor floating in the form of condensed droplets. A hazy environment typically causes an obstruction to vision of less than 1 km of visibility. Such a foggy weather introduces particulate water droplets which in turn cause scattering of light. The scattering of light represents directional changes of traveling light as it collides with airborne particles. The light scattering differs by the wavelength of light and the particle size.
Light scattering can be generally divided into Rayleigh scattering and Mie scattering. The Rayleigh scattering applies when the size of the particles that cause the scattering is smaller than the wavelength of light where the scattering energy is inversely proportional to the fourth power of the wavelength. This is exemplified by the blue hue of the sky in a sunny day when air molecules scatter the light by scattering more blue than red. However, the Mie scattering theory applies to the light scattering when the size of the responsible particles is greater than the wavelength of light. Haze follows the Mie scattering theory since the particles of haze have the diameter as large as a few μm to a few tens of μm over the wavelength of 400 nm˜700 nm of visible light. According to the Mie scattering theory, with larger scattering particles like water droplets in the atmosphere, the amount of scattering is less influenced by the wavelength to cause near-even scattering of all the light in the visible light zone. Therefore, when the condition is hazy, objects exhibit a blurred image. A new light source called airlight is generated.
Image quality improvement through correction of the haze distortion may resolve the visibility obstruction and sharpen fuzzy images. In addition, such image quality improvement is a pre-processing step for recognition of the subjects by restoring damaged information on letters, objects, and the like due to the haze.
Dehazing technologies may be roughly categorized into a non-model approach and a model approach.
An example of the non-model approach includes histogram equalization. The histogram equalization is a method for analyzing a histogram of an image to adjust distribution. The histogram equalization is easily achieved and provides an improved effect in some situations, but is not proper for a hazy image having a non-uniform depth in other situations. In addition, the histogram equalization is proper for improving general image quality in some situations, but does not satisfactorily reflect characteristics of an influence that haze has on an image in other situations. For an image containing thick haze, improvement is insignificant in some situations.
The model approach includes modeling an influence that a scattering phenomenon of light due to haze has on an image. A technology includes comparing two or more images obtained in different weathers to estimate and correct a scene depth, thereby correcting distortion due to haze. This technology uses input of at least two images under different weathers. For a real-time implementation, weather changes are monitored and images are stored. In some situations, it is difficult to determine a storage period since it is difficult to predict a weather change cycle. In some situations, images of the same scene are to be captured, which is difficult due to moving objects causing errors during estimation of the degree of haze distortion.
Another technology for correcting distortion due to haze includes estimating and subtracting the amount of change of pixel values of an image due to the haze. This technology is based on the assumption that the haze is uniform, which is best applicable to uniform and thin haze. However, most real world haze tends to be non-uniform. In addition to the assumption that the haze is uniform, the degree of the haze influence further depends on the distance between a camera and an object.
Other image contrast improvement methods, such as histogram equalization or gamma correction, may improve a contrast over an image. In some situations, the image contrast is difficult to improve in a hazy image having a contrast decreasing differentially by depth information of the image.